Supervised visual tracking
The past few years have witnessed significant performance improvement in deep-learning based trackers.
We can roughly divide these trackers into two categories: online-optimized trackers [1]}, [2]}, [3]}, [4]} and offline trackers [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}.
Online-optimized trackers rely on online update methods with dedicated design. The rough position of target objects are figured out by ridge regression with updated template kernels. Then refinement is applied to estimate accurate bounding boxes.
On the other hand, offline trackers learn to match the template and search region in metric space instead.
This category is dominated by Siamese-network based trackers.
The pioneering work SiamFC [12]} extracts features of template and search patches with shared backbone network. Then cross correlation is applied to generate response map for locating the target.
Various efforts have been made in this category, such as better backbone networks [5]}, target-aware attentions [6]}, [15]}, anchor-free regression [7]}, [8]}, and effective template-search fusion [18]}, [19]}.
Efficient tracking [20]}, [21]} with Siamese-based trackers is also explored with pruning [22]}, [23]} and network architecture search [24]}.
Nevertheless, all of these methods require extensive supervised training involving a large number of annotated videos to learn the correspondence between template and search region.
In contrast, our work is an unsupervised learning framework for accurate Siamese-based tracking, which does not require a huge annotation cost.
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